Sonar is a widely used method for determining the position of objects in the ocean. Sonar has been used for navigation and to find mines or other objects of interest on the ocean bottom. The location of objects on the ocean floor might cue an operator as to position of his vessel when he can compare the locations to a map of previously surveyed regions. Sonar scans may also reveal whether changes have occurred in the region. Also, one can develop symbolic feature maps for only data related to significant features identified by sonar. Unlike optical images, sonar images are very noisy. Thus, it is very difficult to identify objects, particularly small objects, in sonar images. Also, it is difficult to compare two sonar images to determine whether the images were taken at the same location. Some images have a very high information content while others are virtually flat or featureless. In some images subtle changes in texture are the only distinguishing feature. Another difficulty in analyzing sonar images results from the fact that the image is dependent upon the angle at which the sonar signal strikes an object. That angle, of course, is determined by the height of the sonar emitting vehicle from the ocean bottom and the distance the objects are from the source of the sonar signals. Objects which are close to the sonar source show very small shadows. Those shadows increase for objects as the distance from the sonar source increases. Thus, the sonar image of any particular object is dependent on both angle and distance from which the sonar signal is transmitted.
There is a need to compare sonar images to determine if they are both taken at the same location. This may be important for navigation or other purposes. After determining that two images had been taken at the same location, it may be important to know whether any changes have occurred in the position or number of objects present in one image as compared to a second image. Prior to the present invention, there has been no way to automatically compare the two images. Rather, trained, experienced technicians have made visual comparisons to determine if two prints or displays were taken of the same location. This process can take a great deal of time. Automatic detection systems developed for optical images do not work well for noisy sonar images. The noisy nature of sonar images often precludes the use of line and edge detection operators of the type used for video images. Pattern recognition techniques used on video images are not applicable to sonar images because the representation of objects on a sonar image is very dependent upon the distance and angle between the object and the source of the sonar. Thus, two images of the same object taken at a different distance and angle may appear to be quite different to the untrained eye. Connectivity analysis which has been used for video images cannot be used on noisy images such as those produced by sonar. Prior information about the scene is not available for sonar images so pattern recognition techniques cannot be used. Thus, there is a need for an automatic method of analyzing sonar images to identify objects in the image and to compare images for matching purposes.
An autonomous underwater vehicle requires an accurate navigational system for covert missions during which the vehicle cannot surface. Sonar has been used for obstacle avoidance while maneuvering but not for such navigation. This invention proposes side scan sonar for navigation.
Development of a side scan sonar overlap navigation system is complicated by the fact that objects which appear in the overlap region of one image may not be the same as objects appearing in the same overlap region on an adjacent pass. The discrepancy results from the fact that the return signal from bottom objects is direction and grazing angle dependent as well as the fact that sonar signals have a low signal-to-noise ratio which results in a somewhat random detection of objects. Nevertheless, objects in the overlap zone have a reasonable probability of being detected on both passes. There is a need for a method to correlate objects seen on one pass with objects seen on the next pass. Such a method should be able to tolerate missing objects. Such method would permit accurate navigation by overlapping and matching side scan sonar images.
SUMMARY OF THE INVENTION
We have developed a method for analyzing sonar images which involves the basic steps of normalizing the image, filtering the image and then displaying the normalized and filtered image. We may also apply a matching algorithm to the filtered and normalized image and a second image. The filtering process equalizes the contrast across the image. Because sonar images can be represented as raster images often containing 480 rows by 512 columns of pixels, one can digitize each pixel on a gray scale and then apply an algorithm to normalize the image. Then, one can convolve the image using filters. We prefer to use low pass filters having a generally round area. We further prefer to apply these filters in a sequence wherein the filter having the lowest area is applied first and subsequent filters having 2, 3 and 4 times the area of smallest low pass filter are used successively. We have found that the number of objects in an image will determine the best size filter to use. If the image has very few objects, one should use a smaller filter. If the image has a large number of objects, a larger filter is required. If one uses the wrong filter he may have either no objects detected or too many detections which leads to excessive matching computations.
We have found that four particular filters are useful in this process. Each filter will generate a certain number of false detections which we call false alarms depending on the conditions selected for normalizing the image. Through experience we have developed a chart of false alarm ranges which is shown in the figures. From this chart we can select the appropriate filter to be used for a particular image. In addition to the low pass filter, we also prefer to apply a spatial differentiation filter to combine the search for highlights and shadows in one step. This differentiation filter is a matrix comprised of one row and a number of columns which is equal to the number of columns in the low pass filter which has been applied to the image. After filtering objects in the image can be more reliably identified. Moreover, filtered images are well, suited for matching. We propose to match features detected twice within the common overlap regions of two adjacent sonar images and use state estimation to reduce navigation errors. By correcting both random and systematic errors in the navigation system, complete area coverage with no missed areas and without undue overlap is ensured. In particular, this capability is important for correcting drift errors associated with inertial navigations systems. Knowledge of navigation errors enables the vehicle's position and the positions of objects in the image to be estimated more accurately.
Other objects and advantages of the invention will become apparent as a description of certain present preferred embodiments of the invention proceeds.